CN114021759A - Method for managing digital factory equipment based on AIOT and MR technology - Google Patents
Method for managing digital factory equipment based on AIOT and MR technology Download PDFInfo
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Abstract
The application discloses a method for managing equipment in a digital factory based on AIOT and MR technologies, which comprises the following steps: acquiring maintenance guide information sent by a processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model; and displaying the maintenance guide information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guide information. According to the method and the device, the time for finding the equipment fault and the time for solving the equipment fault are saved, and the process from finding the equipment fault to solving the equipment fault is shortened, so that the problems of long process and long time from finding the equipment fault to solving the equipment fault are solved, and the purpose of improving the processing efficiency of the equipment fault is achieved.
Description
Technical Field
The application relates to the technical field of digital factories, in particular to a method for managing equipment in a digital factory based on AIOT and MR technologies.
Background
In a digital factory, an equipment administrator is required to patrol equipment so as to manage the equipment, so that equipment faults existing in the equipment can be found in time and the equipment faults can be processed in time when the equipment abnormally operates.
At present, when the equipment has equipment faults, the equipment can give an alarm, and an equipment manager informs professional equipment maintenance personnel of handling the equipment faults before finding out the equipment faults, so that the process from finding the equipment faults to solving the equipment faults is quite long and needs much time.
Disclosure of Invention
The main purpose of the present application is to provide a method for managing devices in a digital factory based on the AIOT and MR technologies, and to solve the existing technical problem of how to improve the processing efficiency of device failures.
In order to achieve the above object, the present application provides a method for managing devices in a digital factory based on the AIOT and MR technologies, which is applied to MR glasses, and the method for managing devices in a digital factory based on the AIOT and MR technologies includes:
acquiring maintenance guide information sent by a processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model;
and displaying the maintenance guide information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guide information.
Optionally, the displaying the maintenance guidance information based on the MR display mode includes:
and displaying the maintenance guidance information in steps based on preset display steps and an MR display mode, and displaying the operation positions corresponding to the steps.
Optionally, after the step of displaying the maintenance guidance information based on the preset display step and the MR display mode, and displaying the operation positions corresponding to the steps, the method includes:
detecting a call instruction input by an equipment administrator to call a technical expert when the equipment fault is not solved;
calling the technical expert based on the calling instruction;
receiving operation guidance provided by the technical expert.
Optionally, after the step of displaying the maintenance guidance information based on the preset display step and the MR display mode, and displaying the operation positions corresponding to the steps, the method further includes:
and recording the operation guide, and binding the operation guide and the equipment fault and then storing the operation guide.
Optionally, the device is a plurality of devices, the device operation data is device operation data of the plurality of devices, and the acquiring maintenance guidance information sent by the processing platform includes:
and acquiring maintenance guide information sent by a processing platform aiming at the equipment operation data of the plurality of equipment, wherein the maintenance guide information is obtained by analyzing the processing platform by combining the relevance among the plurality of equipment and the equipment operation data of the plurality of equipment.
In order to achieve the above object, the present application provides a method for managing digital factory devices based on the AIOT and MR technologies, which is applied to a processing platform on a processing device, and the method for managing digital factory devices based on the AIOT and MR technologies includes:
acquiring equipment operation data acquired by IOT (input/output) and acquiring an equipment fault identification model;
inputting the equipment operation data to the equipment fault recognition model to obtain an equipment fault recognition result; the equipment fault recognition model is obtained by performing iterative training on an untrained equipment fault recognition model based on a training data set in a preset equipment fault database;
and sending maintenance guide information corresponding to the equipment fault identification result to MR glasses so that the MR glasses can display the maintenance guide information based on an MR display mode.
Optionally, before the obtaining of the device fault identification model, the method includes:
acquiring a training data set from the preset equipment fault database, and acquiring an untrained equipment fault identification model;
performing iterative training on the untrained equipment fault identification model based on the training data set to obtain an updated untrained equipment fault identification model, and determining whether the updated untrained equipment fault identification model meets a preset iteration ending condition;
if the updated untrained equipment fault identification model meets the preset iteration end condition, taking the updated untrained equipment fault identification model as the equipment fault identification model;
and if the updated untrained equipment fault identification model does not meet the iteration end condition, returning to the step of performing iterative training on the untrained equipment fault identification model based on the training data set until the updated untrained equipment fault identification model meets the iteration end condition.
Optionally, the preset device failure database stores device operation data of a plurality of devices, correlations among the plurality of devices, and maintenance guidance information corresponding to the device operation data of the plurality of devices and the correlations together.
In addition, to achieve the above object, the present application also provides a processing device, which includes a memory, a processor, and an AIOT and MR technology-based digital factory device management program stored on the memory and executable on the processor, and when the digital factory device management program is executed by the processor, the AIOT and MR technology-based digital factory device management program implements the steps of the AIOT and MR technology-based digital factory device management method as described above.
In addition, to achieve the above object, the present application also provides a computer readable storage medium having stored thereon a digital factory equipment management program based on the AIOT and MR technologies, which when executed by a processor implements the steps of the digital factory equipment management method based on the AIOT and MR technologies as described above.
Compared with the prior art that the processing efficiency of the equipment fault is low due to the fact that the process from the equipment fault finding to the equipment fault solving is long and much time is spent, the method is applied to the MR glasses, and the MR glasses acquire maintenance guide information sent by a processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model; and displaying the maintenance guide information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guide information. The IOT can automatically acquire the equipment operation data of the equipment and provide the equipment operation data to the processing platform so that the processing platform can determine the equipment fault through the equipment fault identification model instead of determining the equipment fault after the equipment gives an alarm, thereby saving the time for finding the equipment fault; after the equipment fault is determined to occur, the maintenance guidance information sent by the processing equipment is displayed in an MR display mode, and an equipment manager is directly instructed to process the equipment fault without informing professional equipment maintenance personnel to process the equipment fault, so that the time for solving the equipment fault is saved. Therefore, the time for finding the equipment fault is saved, the time for solving the equipment fault is also saved, the process from finding the equipment fault to solving the equipment fault is shortened, and the processing efficiency of the equipment fault is improved.
Drawings
FIG. 1 is a schematic flow chart of a first embodiment of a method for managing equipment in a digital factory based on AIOT and MR technology;
FIG. 2 is a schematic flow chart of a second embodiment of the method for managing equipment in a digital factory based on AIOT and MR technology;
fig. 3 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The application provides a method for managing equipment in a digital factory based on AIOT and MR technology, and referring to FIG. 1, FIG. 1 is a schematic flow chart of a first embodiment of the method for managing equipment in a digital factory based on AIOT and MR technology.
While the embodiments of the present application provide embodiments of a method for managing digital factory equipment based on AIOT and MR techniques, it should be noted that although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different from that shown. The method is applied to MR glasses in a digital factory equipment management method based on AIOT and MR technology. For convenience of description, the following description of the respective steps of the digital factory equipment management method based on the AIOT and MR technologies will be omitted. The method for managing the equipment in the digital factory based on the AIOT and MR technology comprises the following steps:
step S10, obtaining maintenance guide information sent by the processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; and the equipment fault is determined by inputting equipment operation data acquired by the IOT to the equipment fault identification model.
In this embodiment, the processing platform runs on a processing device, the processing device may include a computer, a server, etc., MR (Mixed Reality) technology may provide workers with information that they need most in real environments of daily work, including post operation training, equipment maintenance manuals, IOT (Internet of Things) data display, remote expert guidance, etc., and when the workers use MR glasses (glasses using MR technology), they do not affect their observation of real environments at all and can release both hands to operate.
The maintenance instruction information comprises a processing scheme corresponding to the equipment fault information and a holographic maintenance instruction manual, and a corresponding relation exists between the maintenance instruction information and the equipment fault information, namely after the processing equipment receives the equipment operation data, the required maintenance instruction information can be determined through the corresponding relation.
The IOT is a network that interconnects and interworks by using various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners, and the like, and the device operation data is data acquired by the IOT, such as data related to the device operation process, such as voltage, temperature, and the like. It will be appreciated that the plant operating data may fluctuate within a range when the plant is operating normally, and may deviate from this range when the plant is operating abnormally, for example, when the temperature fluctuates between 40 and 60 degrees celsius (e.g., 50-53 degrees celsius), the plant is operating normally, and when the temperature deviates from 40 to 60 degrees celsius (e.g., 80 degrees celsius), the plant is operating abnormally.
The MR glasses may process maintenance guidance information sent by the platform, which establishes a communication connection with the processing device.
And the processing platform processes the equipment operation data after acquiring the equipment operation data through the IOT to obtain an equipment operation state which can be displayed by the MR glasses, and can obtain maintenance guidance information through the equipment fault determined by the equipment fault identification model and the corresponding relation between the equipment fault and the maintenance guidance information in the process of processing the equipment operation data. After receiving the maintenance guidance information sent by the processing platform, the MR glasses may display the maintenance guidance information.
Further, the device is a plurality of devices, the device operation data is device operation data of the plurality of devices, and the obtaining of the maintenance guidance information sent by the processing platform includes:
step a, obtaining maintenance guide information sent by a processing platform aiming at the equipment operation data of the plurality of equipment, wherein the maintenance guide information is obtained by analyzing the processing platform by combining the relevance among the plurality of equipment and the equipment operation data of the plurality of equipment.
In this embodiment, generally, an equipment administrator inspects a plurality of pieces of equipment, for example, a plurality of pieces of equipment on one production line, it can be understood that there is a certain correlation between pieces of equipment on the same production line, and there is a mutual influence between pieces of equipment, for example, the equipment a is responsible for the process a, and the equipment B is responsible for the process B, where the process a is a pre-process of the process B, that is, the process B is executed after the process a is executed, and there is a process correlation between the equipment a and the equipment B, and when there is an equipment fault in the equipment a, the equipment B may be influenced by the equipment fault of the equipment a, and an equipment fault also occurs, and whether or not, what influence is generated may be determined by equipment operation data of the plurality of pieces of equipment. Therefore, it is more accurate to analyze and derive the maintenance guidance information in combination with the correlation of the plurality of apparatuses and the apparatus operation data of the plurality of apparatuses, as compared to analyzing only a single apparatus and deriving the maintenance guidance information.
Step S20, based on MR display mode, displaying the maintenance guidance information for the equipment manager to process the equipment failure based on the maintenance guidance information;
further, the displaying the maintenance guidance information based on the MR display mode includes:
and b, displaying the maintenance guidance information in steps based on the preset display step and the MR display mode, and displaying the operation positions corresponding to the steps.
In this embodiment, the maintenance guidance information is displayed in steps, so that an equipment administrator can conveniently process the equipment fault, and it can be understood that the preset display step corresponds to an equipment fault processing step obtained by splitting the equipment fault processing flow according to the processing sequence, for example, if the equipment fault needs to be processed through the first processing step, the second processing step and the third processing step, the preset display step also has the corresponding first display step, the second display step and the third display step.
It can be understood that, by means of an MR display mode (a display mode based on MR technology), displaying the operation positions corresponding to the steps enables the device administrator to accurately perform device failure processing according to the operation positions, thereby improving the efficiency of device failure processing. It should be noted that when the current step is not completed by the device administrator, the next step is not displayed.
It will be appreciated that displaying the maintenance guidance information in steps can make the equipment troubleshooting process more organized.
Further, after the step of displaying the maintenance guidance information based on the preset display step and the MR display mode and displaying the operation positions corresponding to the steps, the method includes:
step c, detecting a call instruction of calling a technical expert, which is input by an equipment administrator when the equipment fault is not solved;
step d, calling the technical expert based on the calling instruction;
and e, receiving the operation guidance provided by the technical expert.
In the embodiment, for most of equipment faults, an equipment manager can process the faults through maintenance guide information; however, in addition to this, there are some equipment failures that cannot be resolved by the equipment administrator through the maintenance guidance information, and for this equipment failure, after the equipment failure is handled by the equipment administrator through the maintenance guidance information, the equipment failure still exists, and at this time, a call instruction input by the equipment administrator is detected, and the technical specialist specified by the call instruction is called, so as to connect with the technical specialist.
Specifically, after the connection with the terminal of the technical expert is established, the equipment operation data and the current real scene are sent to the terminal, so that the technical expert can better determine the root cause of the problem and determine a corresponding equipment fault solution as if the technical expert is on site, and provide operation guidance according to the equipment fault solution to guide an equipment administrator to perform corresponding operation to solve the equipment fault.
Further, after the step of displaying the maintenance guidance information based on the preset display step and the MR display mode and displaying the operation positions corresponding to the steps, the method further includes:
and f, recording the operation guide, binding the operation guide and the equipment fault, and storing the operation guide and the equipment fault.
In this embodiment, in order to avoid the need of calling a technical expert to assist in processing each time the same equipment fault is encountered, the operation guidance provided by the technical expert may be recorded, so that when the same equipment fault is encountered next time, the equipment manager may be guided to solve the equipment fault through the stored operation guidance, thereby further improving the efficiency of solving the equipment fault.
Compared with the prior art that the processing efficiency of the equipment fault is low due to the fact that the process from the equipment fault finding to the equipment fault solving is long and much time is spent, the method is applied to the MR glasses, and the MR glasses acquire maintenance guide information sent by a processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model; and displaying the maintenance guide information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guide information. The IOT can automatically acquire the equipment operation data of the equipment and provide the equipment operation data to the processing platform so that the processing platform can determine the equipment fault through the equipment fault identification model instead of determining the equipment fault after the equipment gives an alarm, thereby saving the time for finding the equipment fault; after the equipment fault is determined to occur, the maintenance guidance information sent by the processing equipment is displayed in an MR display mode, and an equipment manager is directly instructed to process the equipment fault without informing professional equipment maintenance personnel to process the equipment fault, so that the time for solving the equipment fault is saved. Therefore, the time for finding the equipment fault is saved, the time for solving the equipment fault is also saved, the process from finding the equipment fault to solving the equipment fault is shortened, and the processing efficiency of the equipment fault is improved.
The application also provides a method for managing equipment in a digital factory based on the AIOT and MR technology, and referring to FIG. 2, FIG. 2 is a schematic flow chart of a second embodiment of the method for managing equipment in a digital factory based on the AIOT and MR technology.
While the embodiments of the present application provide embodiments of a method for managing digital factory equipment based on AIOT and MR techniques, it should be noted that although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different from that shown. The method is applied to processing equipment based on AIOT and MR technologies, wherein a processing platform runs in the processing equipment, and the processing platform is an AIOT Internet of things equipment platform. For convenience of description, the following description of the respective steps of the digital factory equipment management method based on the AIOT and MR technologies will be omitted. The method for managing the equipment in the digital factory based on the AIOT and MR technology comprises the following steps:
and step A10, acquiring the equipment operation data acquired by IOT and acquiring an equipment fault identification model.
In this embodiment, after the device operation data is obtained, the device operation data needs to be mined, cleaned, analyzed, and stored.
The IOT is a network that is interconnected and intercommunicated by various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners, and the like, and the acquired information is collected, the device operation data is data collected by the IOT, the device operation data includes data related to the device operation process such as voltage and temperature, and when the device operates normally, the device operation data fluctuates within a certain range, and when the device operates abnormally, the device operation data deviates from the range, for example, when the temperature fluctuates within 40 to 60 degrees centigrade (for example, 50 to 53 degrees centigrade), the device operates normally, and when the temperature deviates from 40 to 60 degrees centigrade (for example, 80 degrees centigrade), the device operates abnormally.
The MR glasses may acquire device operational data and display the device operational data, which establishes a communication connection with the processing device.
The equipment fault recognition model is an AI (Artificial Intelligence) model and is used for intelligently recognizing equipment faults.
Step A20, inputting the equipment operation data to the equipment fault identification model to obtain an equipment fault identification result; the equipment fault recognition model is obtained by performing iterative training on an untrained equipment fault recognition model based on a training data set in a preset equipment fault database;
in this embodiment, the untrained device fault identification model may be constructed by algorithms such as a random forest, an artificial neural network, and a support vector machine, and taking the random forest as an example, the random forest is a forest established in a random manner, the forest is composed of a plurality of decision trees, there is no association between the decision trees, after the device operation data is input into the random forest, each decision tree independently classifies the device operation data, and then the device fault identification result is determined according to the probability corresponding to the device fault information. For example, the device fault information includes device fault information 1, device fault information 2, and device fault information 3, and after the corresponding device operation data is input into the random forest, the probability of the device fault information 1 is 80%, the probability of the device fault information 2 is 14%, and the probability of the device fault information 3 is 6%, so that it can be determined that the probability of the device fault information 1 is the maximum, that is, the device fault identification result is the device fault information 1.
Further, before the obtaining of the equipment fault identification model, the method includes:
and g, acquiring a training data set from a preset equipment fault database, and acquiring an untrained equipment fault identification model.
Step h, performing iterative training on the untrained equipment fault identification model based on the training data set to obtain an updated untrained equipment fault identification model, and determining whether the updated untrained equipment fault identification model meets a preset iteration end condition;
step i, if the updated untrained equipment fault identification model meets the preset iteration end condition, taking the updated untrained equipment fault identification model as the equipment fault identification model;
and j, if the updated untrained equipment fault identification model does not meet the iteration ending condition, returning to the step of performing iterative training on the untrained equipment fault identification model based on the training data set until the updated untrained equipment fault identification model meets the iteration ending condition.
In this embodiment, iterative training is performed on the untrained equipment fault identification model based on a training data set to obtain an updated untrained equipment fault identification model, and it is determined whether the updated untrained equipment fault identification model meets a preset iteration end condition; if the updated untrained equipment fault identification model meets the preset iteration end condition, taking the updated untrained equipment fault identification model as an equipment fault identification model; and if the updated untrained equipment fault identification model does not meet the iteration ending condition, continuously performing iterative training and updating on the updated untrained equipment fault identification model until the updated untrained equipment fault identification model meets the iteration ending condition.
Specifically, iterative training is performed on the untrained equipment fault identification model through a training data set to obtain an updated untrained equipment fault identification model, wherein a preset equipment fault database stores a plurality of pieces of marked equipment fault information. After obtaining the updated untrained equipment fault identification model each time, determining whether the updated untrained equipment fault identification model meets a preset iteration end condition, if the updated untrained equipment fault identification model meets the preset iteration end condition, ending the iteration, and taking the last updated untrained equipment fault identification model as an equipment fault identification model; and if the updated untrained equipment fault identification model does not meet the iteration ending condition, indicating that the updated untrained equipment fault identification model does not meet the use condition, and continuing to perform iterative training and updating on the updated untrained equipment fault identification model until the updated untrained equipment fault identification model meets the iteration ending condition.
It should be noted that the iterative training is a process of training the untrained equipment fault identification model through the labeled equipment fault information for multiple times, and generally, the equipment fault identification model obtained from the untrained equipment fault identification model needs to be updated through multiple rounds of training. It should be noted that, when the preset iteration end condition is that the untrained equipment fault identification model is input or the updated untrained equipment fault identification model is used as the model prediction accuracy reaches the preset accuracy threshold, the iteration is ended.
It should be noted that, besides the training data set, a test data set is also used in the model training process, and similarly, the test data set is from a preset device failure database, where the data amount between the training data set and the test data set is in a certain proportion, for example, 10: 1.
it should be noted that, for the iterative training process, in order to improve the sensitivity of the equipment fault identification model, the untrained equipment fault identification model may be trained for multiple times, specifically, the iterative training process is composed of multiple training and multiple testing, for example, testing 1 time after training 10 times and cycling the training and testing process until the iteration is finished.
Step A30, sending maintenance guide information corresponding to the equipment fault identification result to MR glasses, so that the MR glasses display the maintenance guide information based on an MR display mode.
In this embodiment, there is a corresponding relationship between the device failure recognition result (device failure information) and the maintenance guidance information, and after the device failure recognition result is obtained, the maintenance guidance information can be determined by the corresponding relationship. The MR display mode is based on the MR technology, and the MR (Mixed Reality) technology can provide the most needed information for workers in the real environment of daily work, including post operation training, equipment maintenance manuals, IOT (Internet of Things) data display, remote expert guidance and the like.
Further, the preset device failure database stores device operation data of a plurality of devices, correlations among the plurality of devices, and maintenance guidance information corresponding to the device operation data of the plurality of devices and the correlations together.
In this embodiment, generally, an equipment administrator inspects a plurality of pieces of equipment, for example, a plurality of pieces of equipment on one production line, it can be understood that there is a certain correlation between pieces of equipment on the same production line, and there is a mutual influence between pieces of equipment, for example, the equipment a is responsible for the process a, and the equipment B is responsible for the process B, where the process a is a pre-process of the process B, that is, the process B is executed after the process a is executed, and there is a process correlation between the equipment a and the equipment B, and when there is an equipment fault in the equipment a, the equipment B may be influenced by the equipment fault of the equipment a, and an equipment fault also occurs, and whether or not, what influence is generated may be determined by equipment operation data of the plurality of pieces of equipment. Therefore, it is more accurate to analyze and derive the maintenance guidance information in combination with the correlation of the plurality of apparatuses and the apparatus operation data of the plurality of apparatuses, as compared to analyzing only a single apparatus and deriving the maintenance guidance information.
In the embodiment, the equipment operation data sent by the MR glasses are obtained, and an equipment fault identification model is obtained; inputting the equipment operation data to the equipment fault recognition model to obtain an equipment fault recognition result; the equipment fault recognition model is obtained by performing iterative training on an untrained equipment fault recognition model based on a training data set in a preset equipment fault database; and sending maintenance guide information corresponding to the equipment fault identification result to the MR glasses. The equipment fault is recognized through the trained equipment fault recognition model with high recognition accuracy, the equipment fault is prevented from being recognized through an equipment manager, and the accuracy of equipment fault recognition is improved.
In addition, the present application also provides an apparatus for managing devices in a digital factory based on the AIOT and MR technologies, the apparatus comprising:
the acquisition module is used for acquiring maintenance guide information sent by the processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model;
and the display module is used for displaying the maintenance guidance information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guidance information.
Optionally, the display module is further configured to:
and displaying the maintenance guidance information in steps based on preset display steps and an MR display mode, and displaying the operation positions corresponding to the steps.
Optionally, the apparatus for managing devices in a digital factory based on the AIOT and MR technologies further includes:
the detection module is used for detecting a calling instruction input by an equipment administrator for calling a technical expert when the equipment fault is not solved;
the calling module is used for calling the technical expert based on the calling instruction;
and the receiving module is used for receiving the operation guidance provided by the technical expert.
Optionally, the apparatus for managing devices in a digital factory based on the AIOT and MR technologies further includes:
and the recording module is used for recording the operation guide, binding the operation guide with the equipment fault and then storing the operation guide.
Optionally, the device is a plurality of devices, the device operation data is device operation data of the plurality of devices, and the first receiving module is further configured to:
and acquiring maintenance guide information sent by a processing platform aiming at the equipment operation data of the plurality of equipment, wherein the maintenance guide information is obtained by analyzing the processing platform by combining the relevance among the plurality of equipment and the equipment operation data of the plurality of equipment.
The specific implementation of the device for managing digital factory equipment based on the AIOT and MR technologies in the present application is substantially the same as that of the above-mentioned embodiments of the method for managing digital factory equipment based on the AIOT and MR technologies, and is not described herein again.
In addition, the present application also provides an apparatus for managing devices in a digital factory based on the AIOT and MR technologies, the apparatus comprising:
the receiving module is used for acquiring equipment operation data acquired by IOT and acquiring an equipment fault identification model;
the input module is used for inputting the equipment operation data to the equipment fault identification model to obtain an equipment fault identification result; the equipment fault recognition model is obtained by performing iterative training on an untrained equipment fault recognition model based on a training data set in a preset equipment fault database; the preset equipment fault database stores equipment operation data of a plurality of pieces of equipment, relevance among the plurality of pieces of equipment and maintenance guide information which corresponds to the equipment operation data of the plurality of pieces of equipment and the relevance.
And the sending module is used for sending the maintenance guide information corresponding to the equipment fault identification result to the MR glasses so that the MR glasses can display the maintenance guide information based on an MR display mode.
Optionally, the apparatus for managing devices in a digital factory based on the AIOT and MR technologies further includes:
the acquisition module is used for acquiring a training data set from the preset equipment fault database and acquiring an untrained equipment fault recognition model;
the training module is used for carrying out iterative training on the untrained equipment fault identification model based on the training data set to obtain an updated untrained equipment fault identification model and determining whether the updated untrained equipment fault identification model meets a preset iteration end condition; if the updated untrained equipment fault identification model meets the preset iteration end condition, taking the updated untrained equipment fault identification model as the equipment fault identification model; and if the updated untrained equipment fault identification model does not meet the iteration end condition, returning to the step of performing iterative training on the untrained equipment fault identification model based on the training data set until the updated untrained equipment fault identification model meets the iteration end condition.
The specific implementation of the device for managing digital factory equipment based on the AIOT and MR technologies in the present application is substantially the same as that of the above-mentioned embodiments of the method for managing digital factory equipment based on the AIOT and MR technologies, and is not described herein again.
In addition, this application still provides a treatment facility. As shown in fig. 3, fig. 3 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
It should be noted that fig. 3 is a schematic structural diagram of a hardware operating environment of a processing device.
As shown in fig. 3, the processing apparatus may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the processing device may also include RF (Radio Frequency) circuitry, sensors, audio circuitry, WiFi modules, and the like.
Those skilled in the art will appreciate that the processing device configuration shown in fig. 3 does not constitute a limitation of the processing device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 3, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an AIOT and MR technology-based in-digital factory equipment management program. Among them, the operating system is a program that manages and controls hardware and software resources of the processing device, supporting the execution of the digital factory device management program and other software or programs based on the AIOT and MR technologies.
In the processing device shown in fig. 3, the user interface 1003 is mainly used for connecting a terminal, and performing data communication with the terminal, such as receiving a request sent by the terminal; the network interface 1004 is mainly used for the background server and performs data communication with the background server; the processor 1001 may be configured to invoke the AIOT and MR technology based on digital factory device management program stored in the memory 1005 and perform the steps of the AIOT and MR technology based on digital factory device management method as described above.
The specific implementation of the processing device in the present application is substantially the same as the embodiments of the device management method in a digital factory based on the AIOT and MR technologies, and is not described herein again.
Furthermore, an embodiment of the present application also provides a computer-readable storage medium, which stores an AIOT and MR technology-based digital factory equipment management program, and when the digital factory equipment management program is executed by a processor, the AIOT and MR technology-based digital factory equipment management program implements the steps of the AIOT and MR technology-based digital factory equipment management method as described above.
The specific implementation of the computer-readable storage medium of the present application is substantially the same as the above-mentioned embodiments of the method for device management in a digital factory based on the AIOT and MR technologies, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, a device, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A method for managing equipment in a digital factory based on AIOT and MR technology is applied to MR glasses and comprises the following steps:
acquiring maintenance guide information sent by a processing platform; the maintenance guidance information is used for guiding the solution of the equipment fault determined by the equipment fault identification model; the equipment fault is determined by inputting equipment operation data acquired by IOT to the equipment fault identification model;
and displaying the maintenance guide information based on an MR display mode so that an equipment manager can process the equipment fault based on the maintenance guide information.
2. The method of claim 1, wherein the displaying the maintenance guidance information based on the MR display comprises:
and displaying the maintenance guidance information in steps based on preset display steps and an MR display mode, and displaying the operation positions corresponding to the steps.
3. The method as claimed in claim 2, wherein the step of displaying the maintenance guidance information in steps based on the preset display step and the MR display mode, and after displaying the operation positions corresponding to the steps, comprises:
detecting a call instruction input by an equipment administrator to call a technical expert when the equipment fault is not solved;
calling the technical expert based on the calling instruction;
receiving operation guidance provided by the technical expert.
4. The method as claimed in claim 3, wherein after the step of displaying the maintenance guidance information based on the preset display step and the MR display mode and displaying the operation positions corresponding to the steps, the method further comprises:
and recording the operation guide, and binding the operation guide and the equipment fault and then storing the operation guide.
5. The method of claim 1, wherein the device is a plurality of devices, the device operation data is device operation data of the plurality of devices, and the obtaining maintenance guidance information sent by the processing platform comprises:
and acquiring maintenance guide information sent by a processing platform aiming at the equipment operation data of the plurality of equipment, wherein the maintenance guide information is obtained by analyzing the processing platform by combining the relevance among the plurality of equipment and the equipment operation data of the plurality of equipment.
6. A method for managing digital factory equipment based on AIOT and MR technology is characterized by being applied to a processing platform on processing equipment, and comprises the following steps:
acquiring equipment operation data acquired by IOT (input/output) and acquiring an equipment fault identification model;
inputting the equipment operation data to the equipment fault recognition model to obtain an equipment fault recognition result; the equipment fault recognition model is obtained by performing iterative training on an untrained equipment fault recognition model based on a training data set in a preset equipment fault database;
and sending maintenance guide information corresponding to the equipment fault identification result to MR glasses so that the MR glasses can display the maintenance guide information based on an MR display mode.
7. The method of claim 6, wherein obtaining the equipment fault identification model is preceded by:
acquiring a training data set from the preset equipment fault database, and acquiring an untrained equipment fault identification model;
performing iterative training on the untrained equipment fault identification model based on the training data set to obtain an updated untrained equipment fault identification model, and determining whether the updated untrained equipment fault identification model meets a preset iteration ending condition;
if the updated untrained equipment fault identification model meets the preset iteration end condition, taking the updated untrained equipment fault identification model as the equipment fault identification model;
and if the updated untrained equipment fault identification model does not meet the iteration end condition, returning to the step of performing iterative training on the untrained equipment fault identification model based on the training data set until the updated untrained equipment fault identification model meets the iteration end condition.
8. The method of claim 6, wherein the pre-defined equipment failure database maintains equipment operational data for a plurality of equipment, associations between the plurality of equipment, and maintenance guidance information corresponding collectively to the equipment operational data and the associations for the plurality of equipment.
9. A processing device comprising a memory, a processor and an AIOT and MR technology based digital factory device management program stored on the memory and executable on the processor, the AIOT and MR technology based technology implementing the steps of the AIOT and MR technology based digital factory device management method according to any one of claims 6 to 8 when the digital factory device management program is executed by the processor.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores thereon an AIOT and MR technology based digital factory equipment management program which, when executed by a processor, implements the steps of the AIOT and MR technology based digital factory equipment management method according to any one of claims 1 to 5, 6 to 8.
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